Automatic Semantic Rating and Abstraction of Literature

ABSTRACT

Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS (CLAIMING BENEFIT UNDER 35U.S.C. 120)

None.

FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT STATEMENT

None.

MICROFICHE APPENDIX

Not applicable.

INCORPORATION BY REFERENCE

None.

FIELD OF THE INVENTION

The invention generally relates to systems and methods for automaticallyextracting plot and character elements from works of literature atvarying levels of abstraction, thereby enabling and providing automatedcomparison between works of literature and recommendation of alternativeworks of literature.

BACKGROUND OF INVENTION

In the context of the present disclosure and the related art, “works ofliterature” will be used to refer to textual writings which are readilyencoded into computer files, such as ASCII text, ANSI text, HypertextTransfer Markup Language (HTML), eXtensible Markup Language (XML),portable document format (PDF), word processor files (e.g. *.doc,*.docx, *.odt, *.wpd, etc.), ebook files and the like. The content ofthese files may represent digital novels, books, textbooks, referencebooks, poetry, lyrics, magazines, journals, short stories, catalogs,research papers, user manuals and the like, each of which may have astructural syntax such as a table of contents, and index, one or morechapters with one or more sections and subsections. These works will bereferred to collectively as “digital literature” for the purposes of thepresent disclosure.

Many online services which provide access to digital literature, such asonline book stores, online libraries and online research centers attemptto provide suggestions for similar literary works to users when theysearch for or purchase a particular literature item. Suchrecommendations can increase sales, improve customer affinity, and leadto better research of a subject matter.

SUMMARY OF EXEMPLARY EMBODIMENTS OF THE INVENTION

Deep semantic analysis is performed on an electronic literary work inorder to detect plot elements and optional other storyline elements suchas characters within the work. Multiple levels of abstract are generatedinto a model representing the literary work, wherein each element ineach abstraction level may be independently rated for preference by auser. Through comparison of multiple abstraction models and one or moreuser rating preferences, one or more alternative literary works may beautomatically recommended to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The description set forth herein is illustrated by the several drawings.

FIG. 1 illustrates an overall logical process according to the presentinvention.

FIG. 2 provides additional details of a logical process according to thepresent invention for building a multi-layer abstraction model of theanalyzed literary work.

FIG. 3 provides an illustration of such a multi-layer abstraction model,which can be encoded in a data structure suitable for use by anautomated process to compare to other models of other literary works.

FIG. 4 allows visualization of comparison of two models of two differentliterary works as performed in at least one embodiment of the invention.

FIG. 5 sets forth a generalized architecture of computing platformssuitable for at least one embodiment of the present and the relatedinventions.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S) OF THE INVENTION

The inventors of the present and the related invention have recognizedproblems not yet recognized by those skilled in the relevant arts.Today, comparison of two or more digital literary pieces is “shallow”and largely based on high level concepts, genres, or main plot elements.For example, a person that likes one suspense/thriller literary work islikely to consider other suspense/thriller literary works simply becausethey belong to the same genre of literature. So, by “shallow”, we meancomparison of literature pieces in the present art typically onlyextends to one level of analysis, namely genre. In addition, analysis ofplot elements within each work of literature requires human effort tounderstand and comprehend the concepts of interest that make literarypieces similar to one another.

The present inventors have recognized this problem of manually drivencomparisons of literature, and, having reviewed the available technologyto address the problem, have found no suitable, useful solution. Thus,the present invention was developed.

The inventors further realized that the present-day methodology alsofails to consider the nuances or deep semantics that make two literarypieces similar both conceptually and in writing style. And, in furtherconsideration of the virtual explosion of digitally published pieces ofliterature, including digital books (eBooks), digital sheet music, webpages, blogs, self-published books, etc., the task of manually reading,comprehending, and comparing digital literature is rapidly becomingunwieldy, while customers of online digital literature expect instantaccess to similar works with little or no human input or wait time.

The invention disclosed herein addresses the current problems as well asaddresses the future demands of such online systems for accessing,distributing, and purchasing digital literature.

Following the disclosure of illustrative embodiments of the presentinvention, a discussion of the reviewed available technology and acomparison to the presently-disclosed methods and systems is provided.

Deep Semantics.

The term “deep semantic” relationships, for the purposes of the presentdisclosure, is meant to refer to relationships between informationentities in a given context and how they relate to each other. They canbe the occurrence of triple store terms or entities or they can be theoccurrence with a relationship of those entities. For example,(Mutation, Cancer, Organ) would be a semantic relationship, identifyingthat mutations, cancer and specific organ ontologies have a deeprelationship. Further, a deep semantic analysis system sometimesassociates a specific relationship (mass, ?indicates, metastasis), wherethe combination and synonyms for “indicates” would mean the cancer hasmetastasized.

The term deep semantic relationship may also refer to the relationshipof terms in a specific ontology and their similarity when expressed inpassages of text based on the how they are typically expressed usingsequence matching algorithms for text analysis. For example, thewell-known Smith-Waterman sequence-matching algorithm measures thelengths of the longest similar subsequence between two texts, which isthen a measured or detected semantic relationship between those texts.

Deep semantic relationships consider the meaning of words within thecontext and structure of a sentence. They signify a “deep” understandingthe meaning of words that comprise a relationship within the sentence.Deep semantic relationships are usually developed with a very specificuse case in mind. For example, consider the sentence “John bought breadat the store.” From this, a relationship like sold (store, bread) may bemined, indicating that the store sold bread. This relationship requiresa deep understanding of what a store is (a retailer that sellsconsumable goods) and that bread is one of those items.

For example, one “specific use” in which deep semantic analysis has beenproposed is the deep semantic interpretations of legal texts as proposedby L. Thorne McCarty of Rutgers University (Association of ComputerMachinery (ACM), 971-1-59593-680).

One may contrast deep semantic relationships with shallow semanticrelationships, that latter of which usually only consider the structureof parts of speech within a sentence, and not necessarily the meaningsof those words. An example shallow relationship may simply be of theform sentence (subject, verb, object). In the above example, this wouldbe sentence (john, bought, bread). These terms don't signify any specialmeaning, but their parts of speech form a shallow relationship called“sentence”.

Graphical logical forms for representation of text can be created usingone of several known methods, such as that proposed by James F. Allen,Mary Swift, and Will de Beaumont, of the University of Rochester and theInstitute for Human and Machine Cognition (Association for ComputerLinguistics (ACL), anthology document WO8-2227).

GENERAL METHOD AND EMBODIMENTS OF THE INVENTION

Disclosed is a method or process of decomposing a digital literary pieceinto deep semantic relationships at varying levels of abstraction,wherein the first level of abstraction captures as many plot elements aspossible, and wherein each subsequent level represents furtherabstraction of storyline or plot details, until high level conceptsbegin to emerge. From this semantic analysis, user sentiment to theliterary attributes are inferred and used to identify similar literatureat varying levels of abstraction and detail. This method is advantageousto the prior art because it performs deep semantic analysis ofliterature at varying levels of abstraction. Doing so allows two piecesof digital or digitized literature to be compared to each other atvarying levels of semantic analysis, offering a deep or shallowcomparison as desired. An additional benefit of the presently disclosedmethod is that the current methods focus on shallow semantic analysis,which simply understands similarity of patterns and words as they appearin the text. The disclosed system employs deep semantic analysis, whichanalyzes the concepts of the text at a deeper level than pattern andterm or key word matching.

A general method according to the invention, shown in FIG. 1, proceedsas follows:

-   1. A human user reads a first literary work (115) and rates it    online. The rating (11) is received (110) by a computer system    according to the present invention.-   2. The computer system, equipped with certain components to perform    logical processes disclosed herein, performs deep analysis (101) of    the digital literary work (115) at various layers of abstraction, as    set forth in more details in the following paragraphs.-   3. Based on deep analysis (103) of the user's literary work    consumption history (102), significant plot elements and attributes    (116) that impact the user's ratings are inferred (104, 105) at the    various levels of abstraction.-   4. Deep analysis of two or more books may be performed (106), and    this system may be used to compare them to each other to determine    similarity. Ultimately, one or more recommendations or suggestions    (107, 108) are made to the user via a user interface for other    literary works which the user may find interesting according to one    of the additional levels of abstraction beyond the first level (e.g.    beyond genre or author).

Example Logical Process Implementation.

The following logical process according to the invention performs thedeep analysis and inferring as mentioned in the foregoing generalprocess description:

-   1. The system ingests (201) a digital or digitized literary work    (200) corresponding to the literary work for which a rating was    received.-   2. The system extracts (203) plot points in as much detail as    possible:    -   a. Deep semantic relationships are used to convert (202) the        unstructured text to structured annotations, revealing (203)        detailed plot elements on a per-sentence basis.    -   b. The plot points are maintained in sequential order.-   3. The system introduces (204) a layer of abstraction model (210) on    the previous layer's plot elements to express fewer plot elements at    a higher level of abstraction (e.g. less details).    -   a. The previous layer's plot elements are grouped and annotated        with umbrella annotations representing the grouping.    -   b. Introduction of additional layers of abstraction is repeated        (205, 203, 204) to produce increasing levels of abstraction        (210) of the plot elements until higher level concepts may not        be extracted.    -   c. The process of adding layers stops when a root node is        reached (205), such as the book's genre (e.g. “mystery”,        “drama”, etc.).-   4. The same process is performed, optionally, for additional digital    literary works.-   5. Literary works, such as novels or magazines, may be compared    (206) to one another at varying levels of abstraction, allowing    shallow or deep comparison to one another, and identification (207)    of alternative literary works which may be interesting or of use to    the user at any particular level (layer) of abstraction.

Structured Annotations.

In the foregoing steps, the term “structured annotations” refers to atleast one available embodiment of metadata (data about data). Accordingto this exemplary embodiment, the structure annotation constitutesinformation regarding where that data can be found in a given passage ortext, and contains the raw value of the selected text, keywords orterms. It usually has a preferred or interpreted value for the term,usually also contains further metadata describing that term in aspecific context.

For example, the text “Peter” may be designated as an annotation withmetadata: Noun—parts of speech, Lead Character, Brother to Kayla.

Another example, the text “Sam and Sarah felt anguish over the loss ofthe wood” may be denoted as the raw value of an annotation, withmetadata “Sadness”, where the term “sadness” is derived from the deepsemanatic analysis of the text to not only parse the phrase forstructure, but also determine the meaning of the words.

Example of Operation.

For illustration of how embodiments of the invention accomplish theseobjectives, we now turn to an example processing of text from awell-known work of literature, a paragraph from William Golding's “Lordof the Flies”:

-   -   “He took off his glasses and held them out to Ralph, blinking        and smiling, and then started to wipe them against his grubby        wind-breaker. An expression of pain and inward concentration        altered the pale contours of his face. He smeared the sweat from        his cheeks and quickly adjusted the spectacles on his nose.”

Using deep semantic analysis, the system decomposes the excerpt in thisexample into the first layer of plot points (i.e. the most detailed):

Layer 1 (most detailed, least abstract):

a. He removed his glasses.

b. He offered his glasses to Ralph.

c. He blinked.

c. He smiled.

d. He wiped his glasses on his wind-breaker.

e. His wind breaker was grubby.

f. He showed pain.

g. He showed inward concentration.

h. His face was pale.

i. His face expressed pain and inward concentration.

j. He wiped the sweat from his face.

k. He put his glasses on.

l. He adjusted his glasses.

The system then proceeds to decompose and add a second layer ofincreasing abstraction (i.e. decreasing level of detail):

Layer 2 (more abstract than Layer 1):

a. He offered his glasses to Ralph.

b. He was happy.

c. He cleaned his glasses.

d. He expressed pain and inward concentration.

e. He put his glasses on.

In this stage, the system uses a hypothetical user's history ofconsumption to determine which of the items 1(a) through 1(l) werecorrespond to similar abstraction items in other novels the user hasconsumed (e.g. terms which are likely of interest to the user due to theuser's repeated selection of texts with corresponding terms, plotelements, etc.), and accordingly, the system selects items 1(b), 1(d),1(e), 1(f), 1(g), and 1(k). In this example, the system abstracted items1(f) and 1(g) by combining them, and abstracted other items by removingdetails and/or substituting certain terms for more general or broaderterms (e.g. substituted “happy” for “smiled” where “smile” is a form orspecies of happiness).

Those familiar with deep semantic analysis of text will recognize thatthese functions are within the skill of those in the art to implement.For example, previously mentioned publication “Deep Semantic Analysis ofText” by James F. Allen, Mary Swift, and Will de Beaumont provides atleast one publicly available method for deep semantic analysis togenerate an abstraction from a detailed block of text. And, suchanalysis is not limited to novels, but may be applied to any text-baseddigital work, such as magazines, journals, research papers, etc. It hasbeen illustrated that even legal texts can be analyzed using deepsemantics, such as disclosed in “Deep Semantic Interpretations of LegalTexts” by L. Thorne McCarty of Rutgers University (USA).

Now, a third iteration of the abstraction process may be performed toyield another, less detailed model of the text under analysis:

Layer 3 (less detailed than Layer 2 or Layer 1):

a. He cleaned his glasses with pain and inward concentration.

b. He put his glasses on.

And, it can be iterated yet again:

Layer 4 (less detailed, more abstract than Layer 3):

a. He had glasses.

At this point, the processing of this short excerpt of text could endbecause it has reached a single item of abstraction. As one can readilyimagine, when analyzing an entire digital literary work which hashundreds or thousands times the length of this excerpt, the layers ofabstraction could be many more than this example.

Example of Model of an Entire Novel.

We now turn to FIG. 3 which illustrates a semantic model of abstractionof the entire novel, not just the excerpt of the previous example. Thismodel representation shows multiple levels of abstraction (301, 302,303, 304, 305, and 306) which lead to a root level, such as a genre fora novel. Each item is denoted by a layer number N followed by an itemordinal letter (x) in the format of N(x), and a user rating is shown instars (4 stars being a highly rated item, 1 star being a lowly rateditem).

The relationship lines of FIG. 3 are provided to assist the reader inunderstanding how each item in each layer relates to or leads to one ormore items in the next, more abstract layer. Those ordinarily skilled inthe art will recognize that the results of semantic analysis are notalways two dimensional or planar as shown here, but the illustration isuseful for understanding the relationships between items and layers.

In at least one embodiment of the present invention, each item of themulti-layer abstraction model can be represented by a set of attributesas follows, which is conducive to representation in database records,for example:

[<user_rating>-<node>-<level>]

where <user_rating> is a representation of the user's rating (e.g.number of stars, etc.), <node> is the item identifier within a layer(e.g. the ordinal letter of FIG. 3), and <level> is the abstractionlayer (e.g. could be absolute such as 1, 2, 3, 4, etc., or relative suchas +1, +2, +3, etc.).

Such a notation system can also be captured in an XML-like structure,such as:

<literary_work_model> <abstraction_model_item><description>string</description> <user_rating>****</user_rating><node>x</node> <level>N</level> </abstraction_model_item> . . .</literary_work_model>

In such an XML model, the third level (303) of abstraction of the modelshown in FIG. 3 would be captured as follows:

<literary_work_model> <abstraction_model_item> <level>3</level><node>a</node> <description>“plane crash”</description><user_rating>**</user_rating> </abstraction_model_item><abstraction_model_item> <level>3</level> <node>b</node><description>“some survive”</description> <user_rating>**</user_rating></abstraction_model_item> <abstraction_model_item> <level>3</level><node>c</node> <description>“survivors organized into 2societies”</description> <user_rating>***</user_rating></abstraction_model_item> <abstraction_model_item> <level>3</level><node>d</node> <description>“one society attacks anohter”</description><user_rating>****</user_rating> </abstraction_model_item></literary_work_model>

In such a data structure representing the results of the deep semanticanalysis of a literary work, the processes according to the presentinvention are enabled to compare models of different literary works, andto detect similarities between various levels and items within levels todetermine alternative literary works which may be of interest to theuser based on the user's prior ratings and prior consumption of literaryworks.

Pseudo-code Process. The following pseudo-code process is provided tothe reader for a high-level example of at least one embodiment of theinvention:

-   -   1. System ingests the electronic literature in its entirety,        optionally in part.    -   2. System runs a series of annotators to extract semantic        relationships from text.        -   a. Both deep and shallow semantic relations are detected.        -   b. Deep semantic relationship captures atomic event of            interest, such as “Ralph blows the conch”.    -   3. The system repeats semantic analysis on the annotations made        in the previous iteration.        -   a. For consistency, the annotations may be translated to            plain-text for consistent processing.        -   b. Each iteration of semantic analysis becomes more            generalized, thereby encompassing a broader set of            annotations.    -   4. The process repeats until no further generalization is        possible (e.g. the literature's genre is reached).    -   5. Methods to detect pertinent information/annotations may be        employed.

Example Logical Process for Recommendation Engine.

One embodiment of the present invention to realize a recommendationengine which would, based on the foregoing semantic analyses, makesuggestions to a user of potential interesting alternative works ofliterature is based upon a presumption that most users rarely like orprefer all aspects of every work of literature uniformly throughout thework. Although it is presently common to allow users to make only onerating over the entire work, the presumption is that if given moregranular rating options, they would provide a range of ratings todifferent aspects of the work. For example, the user could be promptedto rate each character in the book, to rate the ending of the plotseparately from the climax, and to rate the potential for a sequel tothe story. Even more granular, the user may be prompted to rate eachevent within the plot, etc.

While such atomic ratings of a work of literature may be useful in somesituations, it is expected that when a system is trying to learn auser's literary preferences, an atomic rating may dilute both what theuser liked and disliked.

Overlaying cross-sections within topographical nodes affords the systemdeep insight into the components of a literary work which usersspecifically favor—i.e., it isolates the essential elements of a piecethat constitute what a user liked. By “cross section”, we mean in thiscontext a portion of sub-graph of the total hierarchy of elements aspreviously disclosed, wherein each layer of the hierarchy represents adifferent level of abstraction. So, for example, as shown in FIG. 3, across section may be taken at “3(c) Survivors organize 2 societies” withits connected elements of a single degree of separation (e.g. 2(c),3(a), 3(b), 4(a) and 4(b)).

The overlaying process then compares this sub-graph to areas orsub-graphs of other models of other literary works, searching formatches or close matches. When matches or close matches are found, thenif the overall rating of both literary works is high, this feature (e.g.survivors, organizing societies) is declared as a likely preferred“literary element”.

By “literary element”, we are referring to features and components of astoryline which can be considered separate from the storyline, but whenarranged into a series and given interrelationships, form the uniquestoryline of the literary work. For example, an literary element at ahigh level may be “natural disaster”, which is an event type of literaryelement. Multiple works of literature may have natural disaster eventsin them, such as earthquakes, storms, floods, disease, etc., but somestorylines may start with a natural disaster, while others may include anatural disaster somewhere within the intermediate storyline. Othertypes of literary elements may include plot events (death, birth,marriage, divorce, infidelity, business transaction, political event,murder, espionage, etc.), moods (comedy, happiness, sadness, horror,mystery, anticipation, etc.), and setting (outdoors, indoors, future,present, past, international, urban, rural, etc.), among others.

Conversely, if a match or close match belongs to two non-preferred worksof literature, the feature can be declared as a non-preferred feature.If the overall ratings of both works of literature are different, thenno change to the features rating would be made. Then, as the samesub-graph is compared to graphs representing yet additional works ofliterature, the preference/non-preference rating of the feature can befurther increased or decreased according to matches and overall ratings.As such, the degree of preference or non-preference can be measured orpredicted with greater certainty for greater numbers of works ofliterature compared, and the degree of preference or non-preference canbe taken from a binary state (prefer or not) to a discrete state (0 to99, where 0 is strongest non-preference and 99 is strongest preference,50 is neutral or unknown).

So, without benefit of the present invention, and based on the fact thatusers often give only an overall rating for the entirety of a literarypiece of work, a problem arises that one rating for an entire literarypiece doesn't afford much insight into what literary elements the readerenjoyed. For example, was it a plot element, the geographical or timeperiod setting for the story, or a particular character which the readerfavored the most?

However, if, according to the present invention, each piece ofliterature which has been given an overall rating by a user isdecomposed into the literary elements (of each piece) using processesaccording to the present invention, and the overall ratings from thesame user can be aggregated across multiple different pieces ofliterature. By determining the correlation between the elements of theseveral user-indicated most preferred pieces of literature, it ispossible to cross-reference the finite literary elements across thepieces of literature to infer commonalities and distinctions that mayhave led to the user's preferred ratings. These literary elements whichare in common with the preferred pieces of literature can be thenassociated with a prediction of a user's preference of a piece ofliterature which he or she has not yet rated.

The same correlation analysis can be performed on the least preferredpieces of literature to determine common elements within them that areassociated with overall non-preference of a literary work.

This enables the system to discern which literary elements the user ispredictably inclined to enjoy, and which elements the user ispredictably disinclined to like. Armed with this information, the systemmay recommend other pieces of literature which the user has not yetrated, but which include those preferred literary elements, whileavoiding the recommendation of pieces of literature which containelements that are not preferred. As a result, the user's experience intaking the recommendations is predicted to be improved as compared tothe current genre-based recommendations of systems of the current art.However, when running the system longitudinally and collecting theuser's eventual ratings of recommended literary works, the system maylearn and fine tune its predictions of preferred and non-preferredliterary elements by adding the newly-received user ratings and modelsof the pieces of literature to the aggregated works datastore, and byupdating the analysis.

For example, referring now to FIG. 4, two models generated for the sameuser for two different literary works are graphically shown. As thoseskilled in the art will recognize, this graphical depiction is for thereader's understanding, but in practice, such a model can be representedin a construct of database records without such a graphicalrepresentation but in a manner which is machine readable and machineuseable. Both models represent literary works which are highly ratedoverall by the user. On the left, a model for a hypothetical mystery isshown, and on the right, a hypothetical model for a romance novel isshown. One can see that there is a difference (401) in the genre, sobased on just analyzing these two models, it is inconclusive whether theuser prefers or not mysteries or romance. However, a bit of a patternemerges through the similarities of an international setting (possiblyeven more specifically a European setting), and the similarity of atragedy (possibly even more specifically an untimely death).

Extending on this analysis and comparison, the more works that are addedto the analysis with a greater range of user ratings (strong like tostrong dislike), the greater the precision of common elements can beinferred. If, for example, after considering twenty rated literarypieces it is found that of thirteen which are highly rated, nine of themare set in Europe, then a strong preference for literary works set inEurope can be inferred. And, if only three of the highly-rated worksinvolve tragedy and untimely death, then a weak to neutral preferencefor this plot element can be inferred. If, out of the twenty consideredonly four are lowly-rated and three of those deal with political themes,then a weak dislike can be inferred from that pattern.

This constitutes a vastly lower signal-to-noise ratio from which toidentify and recommend other pieces tailored to a user's unique tastes,without requiring a large corpus of data to infer the essentialelements. This data allows the system to perform interestingrecommendation methods, such as:

-   -   1. The system may surface other literary works that embody the        most deep rating patterns appearing within the same sequence of        the story.    -   2. The system could also surface pieces that differ from        previous ratings patterns via pivoting on designated rating        elements.        -   a. Example: “I'm looking for something light & humorous . .            . surprise me though”.        -   b. This option recognizes that users might not enjoy            additional selections that too closely align with other            works that they've enjoyed, i.e. “give me something            fresh/new”.

Suitable Computing Platform.

The preceding paragraphs have set forth example logical processesaccording to the present invention, which, when coupled with processinghardware, embody systems according to the present invention, and which,when coupled with tangible, computer readable memory devices, embodycomputer program products according to the related invention.

Regarding computers for executing the logical processes set forthherein, it will be readily recognized by those skilled in the art that avariety of computers are suitable and will become suitable as memory,processing, and communications capacities of computers and portabledevices increases. In such embodiments, the operative invention includesthe combination of the programmable computing platform and the programstogether. In other embodiments, some or all of the logical processes maybe committed to dedicated or specialized electronic circuitry, such asApplication Specific Integrated Circuits or programmable logic devices.

The present invention may be realized for many different processors usedin many different computing platforms. FIG. 5 illustrates a generalizedcomputing platform (500), such as common and well-known computingplatforms such as “Personal Computers”, web servers such as an IBMiSeries™ server, and portable devices such as personal digitalassistants and smart phones, running a popular operating systems (502)such as Microsoft™ Windows™ or IBM™ AIX™, UNIX, LINUX, Google Android™,Apple iOS™, and others, may be employed to execute one or moreapplication programs to accomplish the computerized methods describedherein. Whereas these computing platforms and operating systems are wellknown an openly described in any number of textbooks, websites, andpublic “open” specifications and recommendations, diagrams and furtherdetails of these computing systems in general (without the customizedlogical processes of the present invention) are readily available tothose ordinarily skilled in the art.

Many such computing platforms, but not all, allow for the addition of orinstallation of application programs (501) which provide specificlogical functionality and which allow the computing platform to bespecialized in certain manners to perform certain jobs, thus renderingthe computing platform into a specialized machine. In some “closed”architectures, this functionality is provided by the manufacturer andmay not be modifiable by the end-user.

The “hardware” portion of a computing platform typically includes one ormore processors (504) accompanied by, sometimes, specializedco-processors or accelerators, such as graphics accelerators, and bysuitable computer readable memory devices (RAM, ROM, disk drives,removable memory cards, etc.). Depending on the computing platform, oneor more network interfaces (505) may be provided, as well as specialtyinterfaces for specific applications. If the computing platform isintended to interact with human users, it is provided with one or moreuser interface devices (507), such as display(s), keyboards, pointingdevices, speakers, etc. And, each computing platform requires one ormore power supplies (battery, AC mains, solar, etc.).

CONCLUSION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof, unless specifically stated otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

It should also be recognized by those skilled in the art that certainembodiments utilizing a microprocessor executing a logical process mayalso be realized through customized electronic circuitry performing thesame logical process(es).

It will be readily recognized by those skilled in the art that theforegoing example embodiments do not define the extent or scope of thepresent invention, but instead are provided as illustrations of how tomake and use at least one embodiment of the invention. The followingclaims define the extent and scope of at least one invention disclosedherein.

What is claimed is:
 1. A method for automatic semantic rating andabstraction of literature comprising: extracting by computer system aseries of annotations from a first electronic text; identifying by acomputer system a deep semantic relationship for entries in theannotations to identify literary elements; abstracting iteratively by acomputer system from the literary elements to form two or more levels ina first electronic model representing the electronic text, wherein theliterary elements and deep semantic relationships between the elementsare represented in a hierarchical manner; comparing by a computer systemone or more literary elements and the deep semantic relationships in thefirst electronic model to one or more literary elements of a secondelectronic model representing a second electronic text, wherein thesecond electronic text has an associated user preference indicator;responsive to the comparing finding a match above a pre-determineddegree of similarity, declaring by a computer system the firstelectronic text to have an expected user preference indicator as theuser preference indicator associated with the second electronic text;and responsive to the comparing finding a match above pre-determineddegree of dissimilarity, declaring by a computer system the firstelectronic text to have an expected user preference indicatorcomplementary the user preference indicator associated with the secondelectronic text.
 2. The method as set forth in claim 1 wherein a highestabstraction level of the electronic model comprises a literary elementcategory selected from the group consisting of genre, setting, centralstoryline event, and mood.
 3. The method as set forth in claim 1 furthercomprising detecting deep semantic relationships of a consumptionhistory of the user, and correlating the consumption historyrelationships to the identified events of the electronic text identifyone or more other electronic texts of likely interest to the user. 4.The method as set forth in claim 1 wherein the electronic text comprisesa work of literature selected from the group consisting of an electronicbook, an electronic play, an electronic novel, a transcription of avideo or movie, an electronic magazine, an electronic reference book,and electronic research paper.
 5. The method as set forth in claim 1wherein the extracting, identifying and abstracting are repeated for aplurality of different electronic texts, and wherein the method furthercomprises: aggregating user ratings from the same user across thedifferent electronic texts; cross-referencing one or more elementsacross the different electronic texts to infer commonalities anddistinctions; and inferring one or more literary elements the user isinclined to enjoy according to the aggregated user ratings according tothe commonalities, or according to the distinctions, or according toboth the commonalities and the distinctions; and designating one or moreof the different electronic texts with a likely user rating.
 6. Themethod as set forth in claim 5 wherein the extracting, identifying andabstracting are repeated for a plurality of different electronic textsforms an electronic model for each different electronic text, each modelhaving a plurality of abstraction level, and wherein thecross-referencing comprises overlaying cross-sections withintopographical nodes of two or more of the electronic models.
 7. Themethod as set forth in claim 5 wherein the designating comprisesrecommending a different electronic text that the user is likely toprefer.
 8. The method as set forth in claim 5 wherein the designatingcomprises dissuading against a different electronic text that the useris unlikely to prefer.
 9. A system for automatic semantic rating andabstraction of literature comprising: a processor for performing alogical process; and at least one computer readable, tangible datastorage device, encoded with program instructions for causing theprocessor to; extract a series of annotations from a first electronictext; identify a deep semantic relationship for entries in theannotations to identify literary elements; abstract iteratively from theliterary elements to form two or more levels in a first electronic modelrepresenting the electronic text, wherein the literary elements and deepsemantic relationships between the elements are represented in ahierarchical manner; compare one or more literary elements and the deepsemantic relationships in the first electronic model to one or moreliterary elements of a second electronic model representing a secondelectronic text, wherein the second electronic text has an associateduser preference indicator; responsive to the comparing finding a matchabove a pre-determined degree of similarity, declare the firstelectronic text to have an expected user preference indicator as theuser preference indicator associated with the second electronic text;and responsive to the comparing finding a match above pre-determineddegree of dissimilarity, declare the first electronic text to have anexpected user preference indicator complementary the user preferenceindicator associated with the second electronic text.
 10. The system asset forth in claim 9 wherein a highest abstraction level of theelectronic model comprises a literary element category selected from thegroup consisting of genre, setting, central storyline event, and mood.11. The system as set forth in claim 9 further comprising detecting deepsemantic relationships of a consumption history of the user, andcorrelating the consumption history relationships to the identifiedevents of the electronic text identify one or more other electronictexts of likely interest to the user.
 12. The system as set forth inclaim 9 wherein the electronic text comprises a work of literatureselected from the group consisting of an electronic book, an electronicplay, an electronic novel, a transcription of a video or movie, anelectronic magazine, an electronic reference book, and electronicresearch paper.
 13. The system as set forth in claim 9 wherein theprogram instructions for extracting, identifying and abstracting arerepeated for a plurality of different electronic texts, and wherein theprogram instructions further comprise instructions for: aggregating userratings from the same user across the different electronic texts;cross-referencing one or more elements across the different electronictexts to infer commonalities and distinctions; and inferring one or moreliterary elements the user is inclined to enjoy according to theaggregated user ratings according to the commonalities, or according tothe distinctions, or according to both the commonalities and thedistinctions; and designating one or more of the different electronictexts with a likely user rating.6.
 14. The system as set forth in claim13 wherein the program instructions for extracting, identifying andabstracting are repeated for a plurality of different electronic textsforms an electronic model for each different electronic text, each modelhaving a plurality of abstraction level, and wherein thecross-referencing comprises overlaying cross-sections withintopographical nodes of two or more of the electronic models.
 15. Thesystem as set forth in claim 13 wherein the program instructions fordesignating comprise program instructions for recommending a differentelectronic text that the user is likely to prefer.
 16. The system as setforth in claim 13 wherein the program instructions for designatingcomprise program instructions for dissuading against a differentelectronic text that the user is unlikely to prefer.
 17. A computerprogram product for automatic semantic rating and abstraction ofliterature comprising: a computer readable, tangible data storagedevice; first program instructions for extracting a series ofannotations from an electronic text; second program instructions foridentifying a deep semantic relationship for entries in the annotationsto identify literary elements; third program instructions forabstracting iteratively from the literary elements to form two or morelevels in a first electronic model representing the electronic text,wherein the literary elements and deep semantic relationships betweenthe elements are represented in a hierarchical manner; fourth programinstructions for comparing one or more literary elements and the deepsemantic relationships in the first electronic model to one or moreliterary elements of a second electronic model representing a secondelectronic text, wherein the second electronic text has an associateduser preference indicator; fifth program instructions for, responsive tothe comparing finding a match above a pre-determined degree ofsimilarity, declaring the first electronic text to have an expected userpreference indicator as the user preference indicator associated withthe second electronic text; and sixth program instructions for,responsive to the comparing finding a match above pre-determined degreeof dissimilarity, declaring the first electronic text to have anexpected user preference indicator complementary the user preferenceindicator associated with the second electronic text; wherein the first,second, third, fourth, fifth and sixth program instructions are storedby the computer readable, tangible data storage device.
 18. The computerprogram product as set forth in claim 17 wherein a highest abstractionlevel of the electronic model comprises a literary element categoryselected from the group consisting of genre, setting, central storylineevent, and mood.
 19. The computer program product as set forth in claim17 further comprising seventh program instructions stored by thecomputer readable, tangible data storage device for detecting deepsemantic relationships of a consumption history of the user, andcorrelating the consumption history relationships to the identifiedevents of the electronic text identify one or more other electronictexts of likely interest to the user.
 20. The computer program productas set forth in claim 17 wherein the electronic text comprises a work ofliterature selected from the group consisting of an electronic book, anelectronic play, an electronic novel, a transcription of a video ormovie, an electronic magazine, an electronic reference book, andelectronic research paper.
 21. The computer program product as set forthin claim 17 wherein the program instructions for extracting, identifyingand abstracting are repeated for a plurality of different electronictexts, and wherein the method further comprises: eighth programinstructions for aggregating user ratings from the same user across thedifferent electronic texts; ninth program instructions forcross-referencing one or more elements across the different electronictexts to infer commonalities and distinctions; and tenth programinstructions for inferring one or more literary elements the user isinclined to enjoy according to the aggregated user ratings according tothe commonalities, or according to the distinctions, or according toboth the commonalities and the distinctions; and eleventh programinstructions for designating one or more of the different electronictexts with a likely user rating; wherein the eighth, ninth, tenth, andeleventh program instructions are stored by the computer readable,tangible data storage device.
 22. The computer program product as setforth in claim 21 wherein the program instructions for extracting,identifying and abstracting are repeated for a plurality of differentelectronic texts forms an electronic model for each different electronictext, each model having a plurality of abstraction level, and whereinthe cross-referencing comprises overlaying cross-sections withintopographical nodes of two or more of the electronic models.
 23. Thecomputer program product as set forth in claim 21 wherein the programinstructions for designating comprise program instructions forrecommending a different electronic text that the user is likely toprefer.
 24. The computer program product as set forth in claim 21wherein the program instructions for designating comprise programinstructions for dissuading against a different electronic text that theuser is unlikely to prefer.